Real-Time Adaptive Algorithms for Flight Control Diagnostics and Prognostics

Award Information
Agency:
National Aeronautics and Space Administration
Branch
n/a
Amount:
$99,376.00
Award Year:
2007
Program:
SBIR
Phase:
Phase I
Contract:
NNL07AA72P
Award Id:
83887
Agency Tracking Number:
066015
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
1410 Sachem Place, Suite 202, Charlottesville, VA, 22901
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
120839477
Principal Investigator:
Jason Burkholder
Principal Investigator
(434) 973-1215
burkholder@bainet.com
Business Contact:
Connie Hoover
Business Official
(434) 973-1215
hoover@bainet.com
Research Institution:
n/a
Abstract
Model-based machinery diagnostic and prognostic techniques depend upon high-quality mathematical models of the plant. Modeling uncertainties and errors decrease system sensitivity to faults and decrease the accuracy of failure prognoses. However, the behavior of many physical systems changes slowly over time as the system ages. These changes may be perfectly normal and not indicative of impending fail-ures; however, if a static a priori model is used, modeling errors may increase over time, which can ad-versely effect health monitoring system performance. Clearly, one method to address this problem is to employ a model that adapts to system changes over time. The risk in using data-driven models that learn online to support model-based diagnostics is that the models may ``adapt'' to a system failure, thus ren-dering it undetectable by the diagnostic algorithms. An inherent trade-off exists between accurately track-ing normal variations in system dynamics and potentially obscuring slow-onset failures by adapting to failure precursors that would be evident using static models. Barron Associates, Inc. and the University of Virginia propose an innovative solution that brings together Barron Associates' proven model-based diagnostic and prognostic algorithms with adaptive system identi-fication algorithms enhanced specifically for health monitoring applications that would benefit from online learning.

* information listed above is at the time of submission.

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